Project Summary/Abstract: Computed tomography (CT) is currently the most sensitive test for detecting preclinical lung cancer, which typically presents as an indeterminate pulmonary nodule (IPN). The success of CT for early detection was sealed by the National Lung Screening Trial, in which lung cancer mortality was reduced by 20% relative to chest radiography. However, in the NLST, the screen positivity rate was 24% and 96% of positive screens were false positive. Although these percentages have been partially attenuated by new screening guidelines, it is axiomatic that LDCT screening is highly sensitive for detecting lung cancer, but non-specific, and not without harms. Furthermore, more individuals, both smokers and non-smokers, are diagnosed outside of screening based on the finding of an indeterminate nodule. Methods are urgently needed to better differentiate between individuals with benign disease and those should undergo invasive diagnostic testing. Liquid biopsy has found its way into the cancer lexicon as a reference to tumor biomarkers within blood or other readily accessible biospecimens that reflect the presence and biology of cancer. This precompetitive collaboration brings together academic and industrial partners across the cancer spectrum to advance liquid biopsy technologies for early detection that are viable as clinical tools. Our partnership interleaves the expertise of lung cancer biologists, clinicians, and biostatisticians with industry engineers, converging on a novel liquid biopsy technology ?EFIRM-Liquid biopsy (eLB)? that has already shown high sensitivity detecting circulating DNA mutations in patients with EGFR-mutant lung cancers. To address early lung cancer detection, our academic researchers will develop and validate independent assays for 10 DNA mutations commonly observed in lung cancer as well as introduce a 6-biomarker panel of miRNA to complement and strengthen the blood-based molecular signal of lung cancer. Our industry partners will convert these individual assays to a single array while preserving high sensitivity and specificity. With our clinician scientists, this integrated platform will be validated in patients with screen- or incidentally- detected lung nodules in the size ranges that are most diagnostically challenging. Our overall research proposition is that blood-based biomarkers using the eLB-Lung Cancer Detection Panel (eLB-LCDP) will inform the accurate and robust classification of nodules as benign or malignant. Beyond contributing molecular and technological expertise, standard operating procedures, and annotated clinical materials, we will compare eLB- LCDP with other lung cancer-associated liquid biopsy platforms to be developed in this and related NCI consortia to determine the highest performing biomarkers and platforms that should move to clinical translation, alone or in combination with models that include clinical and imaging variables acquired as part of patient management. Using this orthogonal, multiparametric interrogation approach, we hypothesize that the eLB-LCDP can achieve a classification performance area under the receiver operating characteristic curve (AUC) > 0.85 in near real- time clinical practice.